In this paper, we introduce Dual Chunk Attention (DCA), a new training-free framework to extrapolate the context window of LLMs. We avoid linearly downscaling the posi-tion indices or increasing the base frequency in RoPE (Su et al., 2022). Instead, we opt to reuse the original position ...
Main idea 这篇论文是为了解决长文本问题,提出位置插值(Position Interpolation, PI)方法,用于扩展使用旋转位置编码(Rotary Position Embedding, RoPE)(引用)的大型语言模型(LLMs)的上下文窗口大小。基…
上下文窗口(Context Window)的大小:由于大多数文本来源都太长,无法适应模型的有限上下文窗口,外部数据源需要被切分成很多小块,每块都能适应上下文窗口。 2. 数据必须以一种易于检索最相关文本的格式提供。 8. 探索 下一步是探索一种可以提高模型的推理和计划制定能力的技术,这对于使用LLM驱动应用程序来说是重要的步骤...
Large Language Models (LLMs) operate with a defined limit on the number of tokens they can process at once, referred to as thecontext window. Exceeding this limit can have significant cost and performance implications. Therefore, it is essential to manage the size of the input sent to the L...
Despite being bi-directional, BERT’s understanding is limited to 512 tokens within a context window Its legacy version will be discontinued after January 31, 2025 BERT pricing BERT is open-source and freely available under the Apache 2.0 license. ...
Memory: To remember previous instructions and answers, LLMs and chatbots like ChatGPT add this history to their context window. This buffer can be improved with summarization (e.g., using a smaller LLM), a vector store + RAG, etc. Evaluation: We need to evaluate both the document retrieva...
To train the long-context LLM with CLEX, run the script scripts/train_lm.sh as follows:./scripts/train_lm.shFor training the chat model, run the script scripts/train_chat.sh instead.Note that we use an on-the-fly tokenization, which supports any desired training length without pre-...
Mistral is a family ofa mixture of expertmodels from Mistral AI. Among the newest models is Mistral Large 2 which was first released in July 2024. The model operates with 123 billion parameters and a 128k context window, supporting dozens of languages including French, German, Spanish, Italian...
Experiments on the NaturalQuestions Multi-document QA, KV retrieval, LongBench and timeline reorder tasks, using various models including RoPE models, context windowextended models, and Alibi models, demonstrate the effectiveness and generalizability of our approach. Our method can improve performance by...
Altogether, PagedAttention + vLLM enable massive memory savings as most sequences will not consume the entire context window. These memory savings translate directly into a higher batch size, which means higher throughput and cheaper serving. ...